ReCOGnAIzeアプリは,血管性認知障害と軽度の認知障害を検出するアプリです
Adnan Azam Mohammed1,2, Ashwati Vipin1,2, Yi Jin Leow1,2
1Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|February 13, 2026
まとめ
ReCOGnAIzeは,ゲーム化されたアプリで,AIを使用して,血管性認知障害 (VCI) と軽度の認知障害 (MCI) を正確に検出します. 伝統的な認知テストを上回り,早期発見のためのスケーラブルなソリューションを提供します.
科学分野:
- 医療における人工知能
- 神経科学は神経科学である.
- デジタル・ヘルス・デジタル・ヘルス
背景:
- 血管性認知障害 (VCI) は,認知機能低下の主な原因であり,様々な症状のためにしばしば診断が不十分である.
- 軽度の認知障害 (MCI) 段階でのVCIの早期発見は,適切な介入に不可欠です.
- 現在の診断方法は,VCIのスペクトルを完全に捉えることができず,新しいアプローチを必要とします.
研究 の 目的:
- VCIとMCIを検出するためのタブレットベースの,ゲーム化された,解釈可能なアプリケーションであるReCOGnAIzeの開発と検証.
- 健康な対照群と比較して,VCIおよびMCIを有する個人を特定するReCOGnAIzeのパフォーマンスを評価する.
- ゲーム化されたタスクを通じて,VCIに関連するデジタルバイオマーカーを特定する.
主な方法:
- アジアのコミュニティコホートを含む多段階の横断的な研究.
- 開発段階 (n=200) と検証段階 (n=235) で,包括的な神経イメージングと神経心理学的データがあります.
- 認知機能に関連するデジタル機能を収集するために,タブレットベースのゲーム化されたタスクを使用しました.
主要な成果:
- ReCOGnAIzeは,認知的に健康な個人 (AUC=0.90) からVCI (AUC=0.85) とMCIを区別する上で強力なパフォーマンスを示しました.
- 特定された主要なデジタル機能には,VCIに関連する既知の実行機能の欠陥と一致する処理速度と応答時間の変動性が含まれています.
- ReCOGnAIze複合スコアは,MCIと認知的に健康な個人を区別する上でモントリオール認知評価 (MoCA) を上回った (AUC=0.90 vs. AUC=0.70).
結論:
- ReCOGnAIzeは,VCIとMCIを正確に検出できるスケーラブルで説明可能なAIツールです.
- ゲーム化されたタブレットベースのタスクは,認知機能に関する解釈可能な洞察を提供します.
- ReCOGnAIzeは,VCIとMCIの早期かつ正確な診断のための有望なデジタルソリューションを提供しています.
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